Graduate School and Research Center in Digital Sciences

Mini-batch spectral clustering

Han, Yufei; Filippone, Maurizio

IJCNN 2017, International Joint Conference on Neural Networks, May 14-19, 2017, Anchorage, Alaska, USA / Also on ArXiv

The cost of computing the spectrum of Laplacian matrices hinders the application of spectral clustering to large data sets. While approximations recover computational tractability, they can potentially affect clustering performance. This paper proposes a practical approach to learn spectral clustering based on adaptive stochastic gradient optimization. Crucially, the proposed approach recovers the exact spectrum of Laplacian matrices in the limit of the iterations, and the cost of each iteration is linear in the number of samples. Extensive experimental validation on data sets with up to half a million samples demonstrate its scalability and its ability to outperform state-of-the-art approximate methods to learn spectral clustering for a given computational budget. 

Document Doi Arxiv Bibtex

Title:Mini-batch spectral clustering
Department:Data Science
Eurecom ref:5039
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Bibtex: @inproceedings{EURECOM+5039, doi = {}, year = {2017}, title = {{M}ini-batch spectral clustering}, author = {{H}an, {Y}ufei and {F}ilippone, {M}aurizio}, booktitle = {{IJCNN} 2017, {I}nternational {J}oint {C}onference on {N}eural {N}etworks, {M}ay 14-19, 2017, {A}nchorage, {A}laska, {USA} / {A}lso on {A}r{X}iv}, address = {{A}nchorage, {UNITED} {STATES}}, month = {05}, url = {} }
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